Fingerprint identification is one of the most important biometric related technologies. A fingerprint of a person comprises a distinctive and unique ridge pattern structure. For authentication or identification purposes, this ridge pattern structure can be characterized by endings and bifurcations of the individual ridges which is popularly known as minutiae. As a result, the accuracy of minutia extraction is crucial in the overall success of fingerprint authentication or identification.
Typically, in a good quality fingerprint image, 70–100 minutiae can be located precisely. But, in a poor quality fingerprint image, the number of minutiae that are surely and steadily extractable by common feature extraction algorithms is much less (approximately 20–50). A well-designed enhancement algorithm can dramatically improve the extraction of minutiae.
Usually, ridge pattern detection is performed manually by professional fingerprint experts. However, manual detection is tedious, time-consuming, and expensive and does not meet the performance requirements of the newly developed applications.
Most of the automatic fingerprint feature extraction methods employ conventional image processing techniques for fingerprint feature extraction and suffer from noisy artifacts of the input fingerprint image in practice. Some research in fingerprint image enhancement have been reported, for example, in L. Hong, A. K. Jain, S. Pankanti, and R. Bolle, “Fingerprint Enhancement”, Proc. First IEEE WACV, pp. 202–207, Sarasota, Fla.,1996; P. E. Danielsson and Q. Z. Ye, “Rotation-Invariant Operators Applied to Enhancement of Fingerprints”, Proc. Ninth ICPR, pp 329–333, Rome, 1988; and D. C. Huang, “Enhancement and Feature Purification of Fingerprint Images”, Pattern Recognition, Vol. 26, no. 11, pp. 1221–1671, 1993; the contents of which are hereby incorporated by reference.
However, most of the published approaches for fingerprint image enhancement use conventional image processing technology to improve the clarity of ridge structures. Common fingerprint feature extraction algorithms employ image-processing techniques to detect minutiae. These techniques adopt only a bottom-up computational paradigm, in which no high level knowledge about fingerprint property is used to guide the processing.
Therefore, there is a need for an accurate and efficient technique for generating a geometric pattern based on visual appearances of a biometric image.